Multiple facrtor analysis deals with dataset where variables are organized in groups. Typically, from data coming from different sources of variables. The method highlights a common structure of all the groups, and the specificity of each group. It allows to compare the results of several PCAs or MCAs in a ...

The function Factoshiny of the package Factoshiny proposes a complete clustering strategy that allows you: to draw a hierarchical tree and a partition to describe and characterize the clusters by quantitative and categorical variables to consider lots of individuals thanks to the complementarity of Kmeans and clustering algorithms to consider ...

All you need to do with MCA to analyse a survey is in Factoshiny! MCA – Multiple Correspondence Analysis – is a method for exploring and visualizing data obtained from a survey or a questionnaire, i.e. datasets with categorical variables. The function Factoshiny of the package Factoshiny allows you to perform ...

Correspondence Analysis – CA – is an exploratory multivariate method for exploring and visualizing contingency tables, i.e. tables on which a chi-squared test can be performed. CA is particularly useful in text mining. The function Factoshiny of the package Factoshiny allows you to perform CA in an easy way. You can ...

All you need to do with PCA is in Factoshiny! PCA – Principal Component Analysis – is a well known method for exploring and visualizing data. The function Factoshiny of the package Factoshiny allows you to perform PCA in a really easy way. You can include extras information such as categorical variables, ...

The newest version of R package Factoshiny (2.2) is now on CRAN! It gives a graphical user interface that allows you to implement exploratory multivariate analyses such as PCA, correspondence analysis, multiple factor analysis or clustering. This interface allows you to modify the graphs interactively, it manages missing data, it gives ...

Exploratory multivariate data analysis is studied and has been taught in a “French-way” for a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 5th of March 2018. This MOOC focuses on 5 essential and basic methods, those with ...

A popular approach to deal with missing values is to impute the data to get a complete dataset on which any statistical method can be applied. Many imputation methods are available and provide a completed dataset in any cases, whatever the number of individuals and/or variables, the percentage of ...

Here is a course with videos that present Hierarchical clustering and its complementary with principal component methods. Four videos present a course on clustering, how to determine the number of clusters, how to describe the clusters and how to perform the clustering when there are lots of individuals and/or ...

Here is a course with videos that present Multiple Correspondence Analysis in a French way. The most well-known use of Multiple Correspondence Analysis is: surveys. Four videos present a course on MCA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement MCA ...

Here is a course with videos that present Multiple Correspondence Analysis in a French way. The most well-known use of Multiple Correspondence Analysis is: surveys. Four videos present a course on MCA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement MCA ...

Here is a course with videos that present Correspondence Analysis in a French way. Five videos present a course on CA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement in FactoMineR. With this course, you will be stand-alone to perform and ...

Here is a course with videos that present Principal Component Analysis in a French way. Three videos present a course on PCA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement in FactoMineR, to deal with missing values in PCA thanks to ...

Here is an example of text mining with correspondence analysis. Within the context of research into the characteristics of the wines from Chenin vines in the Loire Valley (French wines), a set of 10 dry white wines from Touraine were studied: 5 Touraine Protected Appellation of Origin (AOC) from Sauvignon vines, and 5 ...

Principal component methods such as PCA (principal component analysis) or MCA (multiple correspondence analysis) can be used as a pre-processing step before clustering. But principal component methods give also a framework to visualize data. Thus, the clustering methods can be represented onto the map provided by the principal component method. ...

This post shows how to perform PCA with R and the package FactoMineR. If you want to learn more on methods such as PCA, you can enroll in this MOOC (everyting is free): MOOC on Exploratory Multivariate Data Analysis Dataset Here is a wine dataset, with 10 wines and 27 sensory attributes (...

Exploratory multivariate data analysis is studied and has been taught in a “French-way” for a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 27th of February. This MOOC focuses on 4 essential and basic methods, those with ...